R Notebook
library(ggeffects)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(ggpubr)Loading required package: ggplot2library(grid)
library(lmerTest)Loading required package: lme4
Loading required package: Matrix
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
steplibrary(devtools)Loading required package: usethissource_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")SHA-1 hash of file is 07e3c11d2838efe15b1a6baf5ba2694da3f28cb1source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")SHA-1 hash of file is 374a4de7fec345d21628a52c0ed0e4f2c389df8efullLong1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitDf.csv", header = T)
orderDf1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/orderDf.csv", header = T)
idShort1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitShort.csv", header = T)
indDiff1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/indDiff.csv", header = T)
idSim1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/simDf.csv", header = T)fullLong2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitDf.csv", header = T)
orderDf2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/orderDf.csv", header = T)
idShort2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitShort.csv", header = T)
indDiff2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/indDiff.csv", header = T)
idSim2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/simDf.csv", header = T)# subset data for traits to only appear once per subject
traitsPerS1 <- fullLong1 %>% distinct(subID, Idx, .keep_all = TRUE)
traitsPerS2 <- fullLong2 %>% distinct(subID, Idx, .keep_all = TRUE)
# subset data for only connected traits to appear per subject
connectDf1 <- fullLong1 %>% filter(connect==1)
connectDf2 <- fullLong2 %>% filter(connect==1)
# convert to factors
fullLong1$connect <- as.factor(fullLong1$connect)
levels(fullLong1$connect) <- list(No = "0", Yes = "1")
fullLong2$connect <- as.factor(fullLong2$connect)
levels(fullLong2$connect) <- list(No = "0", Yes = "1")
# pos neg asymmetry
idShort1$pndiff <- idShort1$pI2Tdeg - idShort1$nI2Tdeg
idShort2$pndiff <- idShort2$pI2Tdeg - idShort2$nI2TdegIdentity Typicality
Traits that are nominated as typical of some identity are evaluated more self-descriptively
Study 1
summary(connect1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ connect + (connect | subID) + (1 | traits)
Data: fullLong1
REML criterion at convergence: 1399579
Scaled residuals:
Min 1Q Median 3Q Max
-4.2589 -0.6865 -0.0179 0.6681 3.7713
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.30258 0.5501
subID (Intercept) 0.04080 0.2020
connectYes 0.05295 0.2301 -0.27
Residual 0.64345 0.8022
Number of obs: 582288, groups: traits, 296; subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01507 0.03449 386.04867 -0.437 0.662
connectYes 0.18996 0.01553 249.72123 12.229 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
connectYes -0.098Study 2
connect2 <- lmer(scale(selfResp) ~ connect + subTend + traitTend + ( connect | subID ) + ( 1 | traits), data=fullLong2)
summary(connect2)
connect2.plot <- ggpredict(connect2, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect2.plotCombined
plotCommAxes(connect1.plot, connect2.plot, "Connect", "Self-Evaluation")Effect of Identity Typicality Depends on Identity Importance
Study 1
Identity importance defined by strength of identification. This is not significant for identity-to-identity centrality.
connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)
boundary (singular) fit: see help('isSingular')connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)boundary (singular) fit: see help('isSingular')summary(connect.streng1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ connect * scale(streng) + subTend + traitTend +
(connect + scale(streng) | subID) + (1 | traits)
Data: fullLong1
REML criterion at convergence: 1399343
Scaled residuals:
Min 1Q Median 3Q Max
-4.2607 -0.6863 -0.0183 0.6682 3.7875
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 1.418e-01 0.376537
subID (Intercept) 4.060e-02 0.201488
connectYes 5.149e-02 0.226906 -0.26
scale(streng) 1.636e-05 0.004045 0.17 -1.00
Residual 6.434e-01 0.802125
Number of obs: 582288, groups: traits, 296; subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.642e-01 4.490e-02 4.046e+02 -14.791 < 2e-16 ***
connectYes 1.860e-01 1.535e-02 2.508e+02 12.118 < 2e-16 ***
scale(streng) -4.933e-03 1.294e-03 2.830e+03 -3.813 0.00014 ***
subTend 6.169e-04 5.132e-04 2.430e+02 1.202 0.23051
traitTend 9.622e-01 5.261e-02 2.944e+02 18.289 < 2e-16 ***
connectYes:scale(streng) 2.741e-02 4.858e-03 2.184e+05 5.641 1.69e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cnnctY scl(s) subTnd trtTnd
connectYes -0.065
scal(strng) 0.008 -0.187
subTend -0.291 -0.015 0.006
traitTend -0.772 -0.003 0.001 0.000
cnnctYs:s() 0.001 -0.039 -0.240 -0.004 0.000
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Study 2
connect.streng2 <- lmer(scale(selfResp) ~ connect * scale(streng) + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.streng2)
connect.streng2.plot <- ggpredict(connect.streng2, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng2.plotCombined
plotCommAxes(connect.streng1.plot, connect.streng2.plot, "Strength of Identification", "Self-Evaluation")Effect of Identity Typicality Depends on Size
Size Difference
connect.size2 <- lmer(scale(selfResp) ~ connect * scale(sizeD) + ( connect + scale(sizeD) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.size2)
connect.size2.plot <- ggpredict(connect.size2, c("sizeD","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Size Differences") + ylab("Self-Evaluation")
connect.size2.plotIdentity Connections
Traits that are nominated as typical of more identities are evaluated more self-descriptively
Study 1
moconn1 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn1)
moconn1.plot <- ggpredict(moconn1, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn1.plotStudy 2
moconn2 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn2)
moconn2.plot <- ggpredict(moconn2, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn2.plotCombined
plotCommAxes(moconn1.plot, moconn2.plot, "Identity-Typicality", "Self-Evaluation")Identity Overlap
Study 1
sm1<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1, control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
summary(sm1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +
scale(traitTend) + (scale(order) | subID) + (1 | traits)
Data: orderDf1
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
REML criterion at convergence: 1354405
Scaled residuals:
Min 1Q Median 3Q Max
-4.3908 -0.6758 -0.0174 0.6682 4.1252
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.13281 0.3644
subID (Intercept) 0.04039 0.2010
scale(order) 0.01523 0.1234 0.21
Residual 0.63400 0.7962
Number of obs: 566898, groups: traits, 296; subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.581e-03 2.465e-02 4.662e+02 0.389 0.697640
scale(T.Sim) 4.057e-02 1.561e-03 4.781e+05 25.995 < 2e-16 ***
scale(streng) -4.605e-03 1.263e-03 5.598e+05 -3.647 0.000265 ***
scale(subTend) 4.451e-03 1.261e-02 2.448e+02 0.353 0.724392
scale(traitTend) 3.865e-01 2.121e-02 2.942e+02 18.219 < 2e-16 ***
scale(T.Sim):scale(streng) 2.308e-02 1.264e-03 5.631e+05 18.256 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(T.S) scl(s) scl(sT) scl(tT)
scale(T.Sm) -0.009
scal(strng) -0.001 -0.075
scal(sbTnd) 0.000 -0.031 0.009
scl(trtTnd) 0.000 -0.012 0.002 0.001
scl(T.S):() -0.002 -0.126 0.093 0.006 -0.001 sm1.plot <- ggpredict(sm1, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm1.plot
Study 2
sm2<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2, control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
summary(sm2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +
scale(traitTend) + (scale(order) | subID) + (1 | traits)
Data: orderDf2
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
REML criterion at convergence: 1374296
Scaled residuals:
Min 1Q Median 3Q Max
-4.1462 -0.6626 -0.0127 0.6630 4.2335
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.15046 0.3879
subID (Intercept) 0.06164 0.2483
scale(order) 0.01028 0.1014 0.23
Residual 0.63152 0.7947
Number of obs: 576170, groups: traits, 296; subID, 247
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.821e-02 2.731e-02 5.000e+02 0.667 0.50534
scale(T.Sim) 3.484e-02 1.521e-03 4.046e+05 22.903 < 2e-16 ***
scale(streng) -3.705e-03 1.264e-03 5.723e+05 -2.930 0.00338 **
scale(subTend) 5.327e-03 1.541e-02 2.450e+02 0.346 0.72985
scale(traitTend) 3.577e-01 2.257e-02 2.941e+02 15.847 < 2e-16 ***
scale(T.Sim):scale(streng) 1.042e-02 1.255e-03 5.642e+05 8.306 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(T.S) scl(s) scl(sT) scl(tT)
scale(T.Sm) -0.014
scal(strng) -0.001 -0.048
scal(sbTnd) 0.000 -0.029 0.003
scl(trtTnd) 0.000 -0.009 0.001 0.001
scl(T.S):() -0.001 -0.096 0.066 0.003 -0.001 sm2.plot <- ggpredict(sm2, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm2.plot
Combined Plot
plotCommAxes(sm1.plot, sm2.plot, "Identity Overlap", "Self-Evaluation")
Identity Distance
Study 1
dm1<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0027177 (tol = 0.002, component 1)summary(dm1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +
scale(traitTend) + (scale(order) | subID) + (1 | traits)
Data: orderDf1
REML criterion at convergence: 1355429
Scaled residuals:
Min 1Q Median 3Q Max
-4.3472 -0.6756 -0.0174 0.6693 4.1093
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.13700 0.3701
subID (Intercept) 0.03995 0.1999
scale(order) 0.01383 0.1176 0.18
Residual 0.63517 0.7970
Number of obs: 566898, groups: traits, 296; subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.469e-03 2.503e-02 4.662e+02 -0.179 0.85838
scale(order) -6.429e-02 7.619e-03 2.474e+02 -8.438 2.73e-15 ***
scale(streng) -4.114e-03 1.258e-03 5.599e+05 -3.272 0.00107 **
scale(subTend) 1.298e-02 1.260e-02 2.445e+02 1.031 0.30370
scale(traitTend) 3.937e-01 2.154e-02 2.941e+02 18.276 < 2e-16 ***
scale(order):scale(streng) -9.300e-03 1.281e-03 4.868e+05 -7.261 3.85e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(r) scl(s) scl(sT) scl(tT)
scale(ordr) 0.091
scal(strng) 0.000 0.014
scal(sbTnd) 0.000 0.005 0.007
scl(trtTnd) 0.000 0.002 0.001 0.000
scl(rdr):() 0.002 0.005 -0.053 0.001 0.001
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0027177 (tol = 0.002, component 1)dm1.plot <- ggpredict(dm1, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm1.plot
Study 2
dm2<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)summary(dm2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +
scale(traitTend) + (scale(order) | subID) + (1 | traits)
Data: orderDf2
REML criterion at convergence: 1374845
Scaled residuals:
Min 1Q Median 3Q Max
-4.0795 -0.6635 -0.0129 0.6634 4.2017
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.154257 0.39276
subID (Intercept) 0.061307 0.24760
scale(order) 0.009124 0.09552 0.22
Residual 0.632151 0.79508
Number of obs: 576170, groups: traits, 296; subID, 247
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.815e-03 2.776e-02 5.023e+02 -0.101 0.919263
scale(order) -5.488e-02 6.222e-03 2.491e+02 -8.820 < 2e-16 ***
scale(streng) -2.985e-03 1.263e-03 5.720e+05 -2.364 0.018082 *
scale(subTend) 1.459e-02 1.543e-02 2.445e+02 0.945 0.345468
scale(traitTend) 3.623e-01 2.285e-02 2.940e+02 15.852 < 2e-16 ***
scale(order):scale(streng) -4.490e-03 1.218e-03 4.239e+05 -3.685 0.000229 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(r) scl(s) scl(sT) scl(tT)
scale(ordr) 0.121
scal(strng) 0.000 0.008
scal(sbTnd) 0.000 0.005 0.001
scl(trtTnd) 0.000 0.002 0.000 0.000
scl(rdr):() 0.000 0.001 -0.051 0.002 0.001
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)dm2.plot <- ggpredict(dm2, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm2.plot
Combined Plot
plotCommAxes(dm1.plot, dm2.plot, "Identity Distance", "Self-Evaluation")
Identity Similarity and Distance Joint Influence on Self-Evaluations (Principal Component)
Study 1
pca1<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf1)
summary(pca1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +
scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
Data: orderDf1
REML criterion at convergence: 1348143
Scaled residuals:
Min 1Q Median 3Q Max
-4.6482 -0.6714 -0.0188 0.6621 4.2617
Random effects:
Groups Name Variance Std.Dev. Corr
traits (Intercept) 0.13137 0.3625
subID (Intercept) 0.04217 0.2053
scale(PCAdist) 0.02131 0.1460 -0.24
Residual 0.62694 0.7918
Number of obs: 566898, groups: traits, 296; subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.219e-03 2.483e-02 4.778e+02 -0.250 0.802
scale(PCAdist) 7.260e-02 9.418e-03 2.480e+02 7.708 3.08e-13 ***
scale(streng) -6.255e-03 1.259e-03 5.596e+05 -4.969 6.74e-07 ***
scale(subTend) 6.149e-03 1.277e-02 2.444e+02 0.482 0.631
scale(traitTend) 3.839e-01 2.110e-02 2.942e+02 18.197 < 2e-16 ***
scale(PCAdist):scale(streng) 9.040e-03 1.314e-03 5.294e+05 6.878 6.06e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(PCA) scl(s) scl(sT) scl(tT)
scl(PCAdst) -0.126
scal(strng) 0.000 -0.016
scal(sbTnd) 0.000 -0.007 0.008
scl(trtTnd) 0.000 -0.003 0.002 0.001
scl(PCA):() -0.003 -0.003 0.103 -0.001 0.000 pca1.plot <- ggpredict(pca1, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca1.plot
Study 2
Combined Plot
Identity Centrality Predicts Strength of Identification
Study 1
I2I1.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort1)
summary(I2I1.streng)
I2I1.streng.plot <- ggpredict(I2I1.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I1.streng.plotStudy 2
I2I2.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort2)
summary(I2I2.streng)
I2I2.streng.plot <- ggpredict(I2I2.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I2.streng.plotCombined Plot
plotCommAxes(I2I1.streng.plot, I2I2.streng.plot, "Identity-to-Identity Centrality", "Strength of Identification")Does positvity of identity predict asymmetries in valenced content?
Study 1
asym.pos1 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort1)
summary(asym.pos1)Study 2
asym.pos2 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort2)
summary(asym.pos2)More trait overlap predicts stronger group identification
Study 1
tcomm.streng1 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort1)
summary(tcomm.streng1)Study 2
tcomm.streng2 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort2)
summary(tcomm.streng2)More identity overlap predicts stronger group identification
Study 1
icomm.streng1 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort1)
summary(icomm.streng1)Study 2
icomm.streng2 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort2)
summary(icomm.streng2)Individual Differences Predict Similarity in Identity Judgments
m <-lmer(scale(posDist) ~ scale(SE) + ( scale(SE) | subID), data=idSim1)
summary(m)
m <-lmer(scale(strengDist) ~ scale(NFC) + ( scale(NFC) | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)Individual Differences Predict Pairwise Overlap in Traits in Common
Study 1
Positive
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)Negative
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)Both
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16558.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.2646 -0.6147 -0.1921 0.5184 7.7602
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4219 0.6495
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.412e-15 4.242e-02 2.440e+02 0.000 1.000
scale(SCC) 3.530e-02 4.242e-02 2.440e+02 0.832 0.406
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16545.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2779 -0.6182 -0.1888 0.5172 7.7610
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3977 0.6307
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.741e-15 4.124e-02 2.440e+02 0.000 1.00000
scale(Ind) 1.588e-01 4.125e-02 2.440e+02 3.851 0.00015 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(Ind) 0.000 m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16554.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2693 -0.6133 -0.1927 0.5197 7.7622
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4137 0.6432
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.445e-15 4.202e-02 2.440e+02 0.000 1.0000
scale(Inter) 9.676e-02 4.203e-02 2.440e+02 2.302 0.0222 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(Intr) 0.000 m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16555.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2661 -0.6148 -0.1935 0.5173 7.7605
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4156 0.6447
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.251e-15 4.212e-02 2.440e+02 0.000 1.0000
scale(SWLS) 8.645e-02 4.212e-02 2.440e+02 2.053 0.0412 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(SWLS) 0.000 m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16559.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2607 -0.6126 -0.1920 0.5190 7.7619
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4228 0.6502
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.017e-15 4.246e-02 2.440e+02 0.000 1.000
scale(IdImp) 1.843e-02 4.247e-02 2.440e+02 0.434 0.665
Correlation of Fixed Effects:
(Intr)
scal(IdImp) 0.000 m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16555
Scaled residuals:
Min 1Q Median 3Q Max
-3.2605 -0.6137 -0.1879 0.5196 7.7573
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4150 0.6442
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.702e-15 4.209e-02 2.440e+02 0.000 1.0000
scale(phi) -8.981e-02 4.209e-02 2.440e+02 -2.134 0.0339 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(phi) 0.000 m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16011.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.6606 -0.6123 -0.1771 0.5399 7.8079
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.02633 0.1623
Residual 0.58030 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -9.045e-16 1.383e-02 2.440e+02 0.00 1
scale(overlap_norm) 6.274e-01 1.383e-02 2.440e+02 45.36 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(vrlp_n) 0.000 m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16340.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.0934 -0.5980 -0.1875 0.5102 7.8369
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.1600 0.4000
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.486e-15 2.711e-02 2.440e+02 0.00 1
scale(H_index) 5.109e-01 2.711e-02 2.440e+02 18.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scal(H_ndx) 0.000 m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16496.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2653 -0.6157 -0.1894 0.5188 7.7542
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4167 0.6455
Residual 0.5809 0.7622
Number of obs: 6860, groups: subID, 245
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.734e-03 4.226e-02 2.430e+02 0.065 0.9485
scale(SE) 7.968e-02 4.226e-02 2.430e+02 1.885 0.0606 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(SE) 0.000 m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16551.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2728 -0.6170 -0.1912 0.5182 7.7561
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4081 0.6388
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.217e-15 4.175e-02 2.440e+02 0.000 1.00000
scale(NFC) 1.223e-01 4.175e-02 2.440e+02 2.929 0.00372 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(NFC) 0.000 m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16557.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2683 -0.6150 -0.1919 0.5192 7.7594
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4192 0.6475
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.432e-15 4.229e-02 2.440e+02 0.00 1.00
scale(DS) -6.260e-02 4.229e-02 2.440e+02 -1.48 0.14
Correlation of Fixed Effects:
(Intr)
scale(DS) 0.000 m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16558.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.2646 -0.6147 -0.1921 0.5184 7.7602
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4219 0.6495
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.412e-15 4.242e-02 2.440e+02 0.000 1.000
scale(SCC) 3.530e-02 4.242e-02 2.440e+02 0.832 0.406
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16558.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2624 -0.6133 -0.1924 0.5193 7.7627
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4205 0.6485
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.489e-15 4.235e-02 2.440e+02 0.000 1.00
scale(MemSE) 5.099e-02 4.236e-02 2.440e+02 1.204 0.23
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16555.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.2557 -0.6146 -0.1910 0.5213 7.7609
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4161 0.6450
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.270e-15 4.214e-02 2.440e+02 0.00 1.0000
scale(PrivCSE) 8.384e-02 4.214e-02 2.440e+02 1.99 0.0478 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(PrvCSE) 0.000 m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16554.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2656 -0.6131 -0.1904 0.5211 7.7600
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4135 0.6431
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.784e-15 4.201e-02 2.440e+02 0.000 1.0000
scale(PubCSE) 9.779e-02 4.202e-02 2.440e+02 2.327 0.0208 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scal(PbCSE) 0.000 m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 16558.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2624 -0.6133 -0.1924 0.5193 7.7627
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4205 0.6485
Residual 0.5803 0.7618
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.489e-15 4.235e-02 2.440e+02 0.000 1.00
scale(MemSE) 5.099e-02 4.236e-02 2.440e+02 1.204 0.23
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 Individual Differences Predict Pairwise Overlap in Identities in Common
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2437 -0.6057 -0.1938 0.3497 4.2370
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4882 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.883e-15 4.538e-02 2.440e+02 0.000 1.000
scale(SCC) -1.252e-03 4.539e-02 2.440e+02 -0.028 0.978
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2427 -0.6055 -0.1928 0.3496 4.2380
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4881 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.267e-15 4.538e-02 2.440e+02 0.000 1.000
scale(Ind) -1.030e-02 4.538e-02 2.440e+02 -0.227 0.821
Correlation of Fixed Effects:
(Intr)
scale(Ind) 0.000 m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15807.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.2452 -0.6055 -0.1906 0.3462 4.2403
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4870 0.6978
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.909e-15 4.533e-02 2.440e+02 0.000 1.000
scale(Inter) 3.543e-02 4.533e-02 2.440e+02 0.782 0.435
Correlation of Fixed Effects:
(Intr)
scale(Intr) 0.000 m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2447 -0.6058 -0.1927 0.3491 4.2381
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4881 0.6986
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.683e-15 4.537e-02 2.440e+02 0.000 1.000
scale(SWLS) 1.320e-02 4.538e-02 2.440e+02 0.291 0.771
Correlation of Fixed Effects:
(Intr)
scale(SWLS) 0.000 m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15806.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.2438 -0.6058 -0.1869 0.3510 4.2439
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4842 0.6959
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.354e-15 4.520e-02 2.440e+02 0.000 1.000
scale(IdImp) 6.303e-02 4.521e-02 2.440e+02 1.394 0.165
Correlation of Fixed Effects:
(Intr)
scal(IdImp) 0.000 m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(phi) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15806.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.2458 -0.6070 -0.1889 0.3487 4.2419
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4846 0.6961
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.393e-15 4.522e-02 2.440e+02 0.000 1.000
scale(phi) -6.035e-02 4.522e-02 2.440e+02 -1.335 0.183
Correlation of Fixed Effects:
(Intr)
scale(phi) 0.000 m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15791.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2373 -0.6025 -0.1833 0.3453 4.2475
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4540 0.6738
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.184e-15 4.382e-02 2.440e+02 0.000 1
scale(overlap_norm) 1.843e-01 4.382e-02 2.440e+02 4.206 3.65e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(vrlp_n) 0.000 m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15781.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2312 -0.6063 -0.1814 0.3389 4.2524
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4352 0.6597
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.340e-15 4.294e-02 2.440e+02 0.000 1
scale(H_index) 2.294e-01 4.294e-02 2.440e+02 5.342 2.11e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scal(H_ndx) 0.000 m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15736
Scaled residuals:
Min 1Q Median 3Q Max
-3.2444 -0.6061 -0.1920 0.3399 4.2418
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.490 0.7000
Residual 0.515 0.7177
Number of obs: 6860, groups: subID, 245
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.381e-04 4.555e-02 2.430e+02 -0.014 0.989
scale(SE) -1.679e-02 4.555e-02 2.430e+02 -0.369 0.713
Correlation of Fixed Effects:
(Intr)
scale(SE) 0.000 m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15807.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.2458 -0.6076 -0.1894 0.3492 4.2414
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4870 0.6979
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.478e-15 4.533e-02 2.440e+02 0.000 1.000
scale(NFC) 3.482e-02 4.533e-02 2.440e+02 0.768 0.443
Correlation of Fixed Effects:
(Intr)
scale(NFC) 0.000 m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(DS) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15807.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.2496 -0.6042 -0.1896 0.3510 4.2412
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4864 0.6974
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.022e-14 4.530e-02 2.440e+02 0.000 1.000
scale(DS) -4.289e-02 4.530e-02 2.440e+02 -0.947 0.345
Correlation of Fixed Effects:
(Intr)
scale(DS) 0.000 m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2437 -0.6057 -0.1938 0.3497 4.2370
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4882 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.883e-15 4.538e-02 2.440e+02 0.000 1.000
scale(SCC) -1.252e-03 4.539e-02 2.440e+02 -0.028 0.978
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2440 -0.6057 -0.1932 0.3499 4.2376
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4882 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.275e-15 4.538e-02 2.440e+02 0.000 1.000
scale(MemSE) 6.208e-03 4.538e-02 2.440e+02 0.137 0.891
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15807.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2471 -0.6060 -0.1898 0.3511 4.2410
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4861 0.6972
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.105e-14 4.529e-02 2.440e+02 0.000 1.000
scale(PrivCSE) 4.581e-02 4.529e-02 2.440e+02 1.011 0.313
Correlation of Fixed Effects:
(Intr)
scl(PrvCSE) 0.000 m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2440 -0.6057 -0.1939 0.3497 4.2369
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4882 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.889e-15 4.538e-02 2.440e+02 0.000 1.000
scale(PubCSE) 2.111e-03 4.539e-02 2.440e+02 0.047 0.963
Correlation of Fixed Effects:
(Intr)
scal(PbCSE) 0.000 m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
Data: idSim1
REML criterion at convergence: 15808.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2440 -0.6057 -0.1932 0.3499 4.2376
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4882 0.6987
Residual 0.5157 0.7181
Number of obs: 6888, groups: subID, 246
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.275e-15 4.538e-02 2.440e+02 0.000 1.000
scale(MemSE) 6.208e-03 4.538e-02 2.440e+02 0.137 0.891
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 Study 2
Positive
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12904
Scaled residuals:
Min 1Q Median 3Q Max
-2.5877 -0.5984 -0.2681 0.5335 7.0228
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.3652 0.6043
scale(idtnSim) 0.0116 0.1077 0.40
Residual 0.5902 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01131 0.04528 182.04489 -0.250 0.803065
scale(SCC) 0.01090 0.04434 165.33351 0.246 0.806200
scale(idtnSim) 0.06080 0.01590 212.20307 3.824 0.000173 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SCC)
scale(SCC) 0.002
scal(dtnSm) 0.195 -0.005m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12903.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.5883 -0.6000 -0.2694 0.5336 7.0241
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36414 0.6034
scale(idtnSim) 0.01159 0.1077 0.40
Residual 0.59026 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01144 0.04522 181.86420 -0.253 0.800564
scale(Ind) 0.02942 0.04405 160.54157 0.668 0.505215
scale(idtnSim) 0.06094 0.01590 212.20479 3.833 0.000167 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(I)
scale(Ind) -0.002
scal(dtnSm) 0.196 0.006m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12903.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5873 -0.5987 -0.2712 0.5337 7.0171
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36502 0.6042
scale(idtnSim) 0.01155 0.1075 0.40
Residual 0.59024 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01130 0.04527 182.18856 -0.250 0.803108
scale(Inter) 0.02522 0.04446 168.46650 0.567 0.571338
scale(idtnSim) 0.06079 0.01589 212.56900 3.825 0.000172 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(I)
scale(Intr) 0.002
scal(dtnSm) 0.195 -0.004m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12903.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.5866 -0.5976 -0.2692 0.5322 7.0200
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.3649 0.6041
scale(idtnSim) 0.0116 0.1077 0.39
Residual 0.5902 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01128 0.04526 182.00258 -0.249 0.80342
scale(SWLS) -0.01474 0.04434 165.32795 -0.332 0.74000
scale(idtnSim) 0.06087 0.01590 212.11200 3.828 0.00017 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SWLS
scale(SWLS) 0.003
scal(dtnSm) 0.192 -0.011m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12901.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5870 -0.5975 -0.2690 0.5323 7.0234
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36008 0.6001
scale(idtnSim) 0.01158 0.1076 0.39
Residual 0.59022 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01113 0.04498 181.96470 -0.248 0.804771
scale(IdImp) 0.06742 0.04396 163.08481 1.534 0.127088
scale(idtnSim) 0.06081 0.01590 212.13012 3.826 0.000172 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(II)
scal(IdImp) 0.001
scal(dtnSm) 0.190 -0.007m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12898.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5739 -0.6014 -0.2704 0.5311 7.0444
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.35334 0.5944
scale(idtnSim) 0.01149 0.1072 0.35
Residual 0.59013 0.7682
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.009882 0.044591 182.205240 -0.222 0.824872
scale(phi) -0.103144 0.043795 165.969844 -2.355 0.019683 *
scale(idtnSim) 0.059839 0.015895 211.196853 3.765 0.000216 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(p)
scale(phi) -0.006
scal(dtnSm) 0.169 0.034m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)Model failed to converge with max|grad| = 0.00351062 (tol = 0.002, component 1)summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12545.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.5885 -0.6210 -0.2473 0.5394 6.9553
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.034411 0.18550
scale(idtnSim) 0.008097 0.08998 -0.08
Residual 0.590868 0.76868
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.021e-05 1.750e-02 1.850e+02 0.003 0.997715
scale(overlap_norm) 5.902e-01 1.778e-02 1.805e+02 33.204 < 2e-16 ***
scale(idtnSim) 5.324e-02 1.416e-02 2.447e+02 3.758 0.000214 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(_)
scl(vrlp_n) 0.011
scal(dtnSm) -0.023 -0.153
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00351062 (tol = 0.002, component 1)m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12709.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.6538 -0.6049 -0.2681 0.5208 6.9964
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.11399 0.3376
scale(idtnSim) 0.01178 0.1085 0.47
Residual 0.59046 0.7684
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.008421 0.026967 181.508677 -0.312 0.755196
scale(H_index) 0.491624 0.026904 183.417584 18.273 < 2e-16 ***
scale(idtnSim) 0.057631 0.015573 210.889610 3.701 0.000274 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(H_)
scal(H_ndx) 0.021
scal(dtnSm) 0.236 -0.094m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12903.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.5909 -0.5992 -0.2714 0.5341 7.0284
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36386 0.6032
scale(idtnSim) 0.01158 0.1076 0.41
Residual 0.59027 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01148 0.04520 181.99825 -0.254 0.799708
scale(SE) 0.04049 0.04413 162.70387 0.917 0.360315
scale(idtnSim) 0.06088 0.01589 212.59468 3.830 0.000169 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(SE)
scale(SE) 0.002
scal(dtnSm) 0.201 -0.003m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12904
Scaled residuals:
Min 1Q Median 3Q Max
-2.5866 -0.5979 -0.2681 0.5341 7.0213
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36524 0.6043
scale(idtnSim) 0.01159 0.1076 0.39
Residual 0.59022 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.011245 0.045282 182.096123 -0.248 0.804155
scale(NFC) -0.009651 0.044190 162.331755 -0.218 0.827402
scale(idtnSim) 0.060755 0.015902 212.257575 3.821 0.000175 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(NFC)
scale(NFC) -0.004
scal(dtnSm) 0.193 0.016m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12902
Scaled residuals:
Min 1Q Median 3Q Max
-2.5839 -0.5988 -0.2765 0.5356 7.0065
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36081 0.6007
scale(idtnSim) 0.01146 0.1070 0.38
Residual 0.59022 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01097 0.04502 182.20703 -0.244 0.807757
scale(DS) 0.06371 0.04429 168.57319 1.438 0.152197
scale(idtnSim) 0.06096 0.01588 212.57124 3.839 0.000163 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(DS)
scale(DS) 0.000
scal(dtnSm) 0.184 0.002 m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12904
Scaled residuals:
Min 1Q Median 3Q Max
-2.5877 -0.5984 -0.2681 0.5335 7.0228
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.3652 0.6043
scale(idtnSim) 0.0116 0.1077 0.40
Residual 0.5902 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01131 0.04528 182.04489 -0.250 0.803065
scale(SCC) 0.01090 0.04434 165.33351 0.246 0.806200
scale(idtnSim) 0.06080 0.01590 212.20307 3.824 0.000173 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SCC)
scale(SCC) 0.002
scal(dtnSm) 0.195 -0.005m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12901.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.5934 -0.6041 -0.2638 0.5359 7.0332
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.35978 0.5998
scale(idtnSim) 0.01165 0.1079 0.42
Residual 0.59027 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01185 0.04496 181.91972 -0.264 0.792403
scale(MemSE) 0.07461 0.04394 164.79246 1.698 0.091375 .
scale(idtnSim) 0.06133 0.01590 212.37079 3.857 0.000152 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(MSE)
scale(MmSE) -0.001
scal(dtnSm) 0.204 0.009m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12901.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.5894 -0.6011 -0.2681 0.5327 7.0283
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36099 0.6008
scale(idtnSim) 0.01157 0.1076 0.40
Residual 0.59025 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01168 0.04503 182.06825 -0.259 0.795566
scale(PrivCSE) 0.06577 0.04401 164.50205 1.495 0.136956
scale(idtnSim) 0.06133 0.01589 212.72319 3.859 0.000151 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(PCSE
scl(PrvCSE) -0.003
scal(dtnSm) 0.197 0.014m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12904
Scaled residuals:
Min 1Q Median 3Q Max
-2.5873 -0.5985 -0.2688 0.5339 7.0228
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36516 0.6043
scale(idtnSim) 0.01157 0.1075 0.39
Residual 0.59023 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.011291 0.045277 182.038936 -0.249 0.80335
scale(PubCSE) 0.009957 0.044193 163.020187 0.225 0.82201
scale(idtnSim) 0.060840 0.015896 212.297480 3.827 0.00017 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(PCSE
scal(PbCSE) -0.001
scal(dtnSm) 0.193 0.007m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) | subID)
Data: idSim2
REML criterion at convergence: 12901.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.5934 -0.6041 -0.2638 0.5359 7.0332
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.35978 0.5998
scale(idtnSim) 0.01165 0.1079 0.42
Residual 0.59027 0.7683
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.01185 0.04496 181.91972 -0.264 0.792403
scale(MemSE) 0.07461 0.04394 164.79246 1.698 0.091375 .
scale(idtnSim) 0.06133 0.01590 212.37079 3.857 0.000152 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(MSE)
scale(MmSE) -0.001
scal(dtnSm) 0.204 0.009Negative
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3284 -0.6148 -0.2177 0.4557 3.8897
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36388 0.6032
scale(idtpSim) 0.01224 0.1106 0.11
Residual 0.60686 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006071 0.045264 186.259128 -0.134 0.893454
scale(SCC) 0.009581 0.045205 185.460170 0.212 0.832387
scale(idtpSim) 0.059056 0.016664 161.832472 3.544 0.000516 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SCC)
scale(SCC) 0.000
scal(dtpSm) 0.065 0.001 m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.3281 -0.6147 -0.2176 0.4553 3.8886
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36354 0.6029
scale(idtpSim) 0.01226 0.1107 0.11
Residual 0.60685 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006021 0.045245 186.258105 -0.133 0.894284
scale(Ind) -0.021601 0.045046 182.480469 -0.480 0.632141
scale(idtpSim) 0.059136 0.016668 161.948869 3.548 0.000508 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(I)
scale(Ind) -0.001
scal(dtpSm) 0.064 -0.007m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3282 -0.6134 -0.2173 0.4568 3.8883
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36332 0.6028
scale(idtpSim) 0.01208 0.1099 0.12
Residual 0.60692 0.7791
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006016 0.045230 186.341070 -0.133 0.894328
scale(Inter) 0.028702 0.045152 185.262560 0.636 0.525764
scale(idtpSim) 0.058990 0.016636 161.914976 3.546 0.000512 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(I)
scale(Intr) 0.002
scal(dtpSm) 0.066 -0.008m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3290 -0.6164 -0.2180 0.4557 3.8944
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.3621 0.6018
scale(idtpSim) 0.0122 0.1104 0.12
Residual 0.6069 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006285 0.045160 186.222869 -0.139 0.889473
scale(SWLS) 0.043654 0.044985 183.530958 0.970 0.333115
scale(idtpSim) 0.059118 0.016657 161.486547 3.549 0.000506 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SWLS
scale(SWLS) -0.002
scal(dtpSm) 0.069 0.004m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3279 -0.6123 -0.2176 0.4560 3.8895
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36381 0.6032
scale(idtpSim) 0.01225 0.1107 0.11
Residual 0.60685 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.005996 0.045261 186.285829 -0.132 0.894759
scale(IdImp) 0.012148 0.045184 185.152606 0.269 0.788342
scale(idtpSim) 0.058989 0.016670 161.883722 3.539 0.000525 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(II)
scal(IdImp) 0.002
scal(dtpSm) 0.063 -0.021m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13046.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.3227 -0.6125 -0.2181 0.4494 3.8938
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.35525 0.5960
scale(idtpSim) 0.01224 0.1106 0.07
Residual 0.60680 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.004952 0.044763 186.655064 -0.111 0.912037
scale(phi) -0.094360 0.044716 184.253757 -2.110 0.036189 *
scale(idtpSim) 0.058446 0.016662 162.781184 3.508 0.000584 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(p)
scale(phi) -0.004
scal(dtpSm) 0.043 0.036m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13032.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3098 -0.6118 -0.2228 0.4504 3.8960
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.32989 0.5744
scale(idtpSim) 0.01175 0.1084 0.04
Residual 0.60680 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.001836 0.043245 189.524045 -0.042 0.96619
scale(overlap_norm) 0.201559 0.045126 205.503201 4.467 1.31e-05 ***
scale(idtpSim) 0.048314 0.016774 170.989681 2.880 0.00448 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(_)
scl(vrlp_n) 0.016
scal(dtpSm) 0.029 -0.169m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13038
Scaled residuals:
Min 1Q Median 3Q Max
-2.3152 -0.6161 -0.2175 0.4545 3.8975
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.34037 0.5834
scale(idtpSim) 0.01162 0.1078 0.11
Residual 0.60696 0.7791
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.003433 0.043873 188.517595 -0.078 0.937706
scale(H_index) 0.165783 0.045210 202.285027 3.667 0.000314 ***
scale(idtpSim) 0.051133 0.016708 168.862963 3.060 0.002573 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(H_)
scal(H_ndx) 0.016
scal(dtpSm) 0.058 -0.145m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3284 -0.6138 -0.2182 0.4557 3.8922
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36384 0.6032
scale(idtpSim) 0.01223 0.1106 0.11
Residual 0.60687 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006086 0.045262 186.250214 -0.134 0.893184
scale(SE) 0.012480 0.045097 183.418709 0.277 0.782293
scale(idtpSim) 0.059006 0.016664 161.789669 3.541 0.000521 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(SE)
scale(SE) 0.000
scal(dtpSm) 0.065 -0.009m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050
Scaled residuals:
Min 1Q Median 3Q Max
-2.3281 -0.6143 -0.2208 0.4536 3.8910
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36154 0.6013
scale(idtpSim) 0.01217 0.1103 0.11
Residual 0.60689 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006008 0.045128 186.219145 -0.133 0.894224
scale(NFC) -0.048851 0.045025 184.855552 -1.085 0.279349
scale(idtpSim) 0.059117 0.016650 162.074632 3.551 0.000503 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(NFC)
scale(NFC) -0.001
scal(dtpSm) 0.064 0.002m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3282 -0.6146 -0.2179 0.4561 3.8894
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36393 0.6033
scale(idtpSim) 0.01224 0.1107 0.11
Residual 0.60686 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006036 0.045268 186.269226 -0.133 0.894072
scale(DS) 0.002183 0.045459 188.853591 0.048 0.961746
scale(idtpSim) 0.059050 0.016668 161.751701 3.543 0.000518 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(DS)
scale(DS) 0.004
scal(dtpSm) 0.064 -0.019m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3284 -0.6148 -0.2177 0.4557 3.8897
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36388 0.6032
scale(idtpSim) 0.01224 0.1106 0.11
Residual 0.60686 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006071 0.045264 186.259128 -0.134 0.893454
scale(SCC) 0.009581 0.045205 185.460170 0.212 0.832387
scale(idtpSim) 0.059056 0.016664 161.832472 3.544 0.000516 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(SCC)
scale(SCC) 0.000
scal(dtpSm) 0.065 0.001 m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3286 -0.6140 -0.2177 0.4553 3.8883
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36289 0.6024
scale(idtpSim) 0.01228 0.1108 0.11
Residual 0.60685 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006015 0.045208 186.140779 -0.133 0.894297
scale(MemSE) -0.029083 0.045189 185.880184 -0.644 0.520637
scale(idtpSim) 0.059346 0.016674 161.957173 3.559 0.000488 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(MSE)
scale(MmSE) 0.001
scal(dtpSm) 0.062 -0.020m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13049.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.3297 -0.6113 -0.2214 0.4541 3.8932
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.35997 0.6000
scale(idtpSim) 0.01227 0.1108 0.11
Residual 0.60687 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006224 0.045037 185.940696 -0.138 0.89024
scale(PrivCSE) -0.059369 0.045057 186.373221 -1.318 0.18925
scale(idtpSim) 0.059620 0.016671 162.342225 3.576 0.00046 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(PCSE
scl(PrvCSE) 0.002
scal(dtpSm) 0.065 -0.020m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13051.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3282 -0.6156 -0.2174 0.4556 3.8888
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36380 0.6032
scale(idtpSim) 0.01224 0.1106 0.11
Residual 0.60686 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006065 0.045260 186.240784 -0.134 0.893540
scale(PubCSE) -0.012395 0.045171 184.882593 -0.274 0.784074
scale(idtpSim) 0.059080 0.016665 161.905271 3.545 0.000513 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(PCSE
scal(PbCSE) -0.001
scal(dtpSm) 0.065 -0.006m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) | subID)
Data: idSim2
REML criterion at convergence: 13050.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3286 -0.6140 -0.2177 0.4553 3.8883
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.36289 0.6024
scale(idtpSim) 0.01228 0.1108 0.11
Residual 0.60685 0.7790
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.006015 0.045208 186.140779 -0.133 0.894297
scale(MemSE) -0.029083 0.045189 185.880184 -0.644 0.520637
scale(idtpSim) 0.059346 0.016674 161.957173 3.559 0.000488 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(MSE)
scale(MmSE) 0.001
scal(dtpSm) 0.062 -0.020Both
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12653
Scaled residuals:
Min 1Q Median 3Q Max
-2.6156 -0.6504 -0.2404 0.5367 8.3469
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4405 0.6637
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.732e-15 4.924e-02 1.880e+02 0.000 1.000
scale(SCC) 5.967e-03 4.924e-02 1.880e+02 0.121 0.904
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12652.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.6156 -0.6509 -0.2403 0.5375 8.3481
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4402 0.6635
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.542e-15 4.922e-02 1.880e+02 0.00 1.000
scale(Ind) 1.721e-02 4.923e-02 1.880e+02 0.35 0.727
Correlation of Fixed Effects:
(Intr)
scale(Ind) 0.000 m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12652.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.6161 -0.6487 -0.2408 0.5375 8.3471
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4398 0.6631
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.859e-15 4.920e-02 1.880e+02 0.000 1.000
scale(Inter) 2.766e-02 4.920e-02 1.880e+02 0.562 0.575
Correlation of Fixed Effects:
(Intr)
scale(Intr) 0.000 m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12652.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.6155 -0.6493 -0.2389 0.5372 8.3423
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4395 0.6629
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.363e-15 4.918e-02 1.880e+02 0.000 1.000
scale(SWLS) -3.235e-02 4.919e-02 1.880e+02 -0.658 0.512
Correlation of Fixed Effects:
(Intr)
scale(SWLS) 0.000 m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12651.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.6133 -0.6468 -0.2401 0.5377 8.3479
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4365 0.6607
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.422e-15 4.902e-02 1.880e+02 0.000 1.000
scale(IdImp) 6.342e-02 4.903e-02 1.880e+02 1.293 0.197
Correlation of Fixed Effects:
(Intr)
scal(IdImp) 0.000 m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12648.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.6191 -0.6523 -0.2411 0.5402 8.3446
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4307 0.6562
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.788e-15 4.871e-02 1.880e+02 0.000 1.0000
scale(phi) -9.884e-02 4.871e-02 1.880e+02 -2.029 0.0439 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(phi) 0.000 m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12149.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.8477 -0.6403 -0.2032 0.5488 8.4500
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.01154 0.1074
Residual 0.56413 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.037e-16 1.291e-02 1.880e+02 0.00 1
scale(overlap_norm) 6.516e-01 1.292e-02 1.880e+02 50.45 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(vrlp_n) 0.000 m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12467.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.7557 -0.6438 -0.2423 0.5303 8.4208
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.1514 0.3891
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.909e-16 3.005e-02 1.880e+02 0.0 1
scale(H_index) 5.349e-01 3.005e-02 1.880e+02 17.8 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scal(H_ndx) 0.000 m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12652.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.6147 -0.6504 -0.2409 0.5369 8.3491
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4398 0.6632
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.139e-15 4.920e-02 1.880e+02 0.000 1.000
scale(SE) 2.739e-02 4.920e-02 1.880e+02 0.557 0.578
Correlation of Fixed Effects:
(Intr)
scale(SE) 0.000 m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12653
Scaled residuals:
Min 1Q Median 3Q Max
-2.6159 -0.6502 -0.2401 0.5367 8.3471
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4405 0.6637
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.703e-15 4.924e-02 1.880e+02 0.000 1.000
scale(NFC) -6.908e-04 4.924e-02 1.880e+02 -0.014 0.989
Correlation of Fixed Effects:
(Intr)
scale(NFC) 0.000 m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12649.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.6132 -0.6473 -0.2407 0.5361 8.3423
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4331 0.6581
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.762e-15 4.884e-02 1.880e+02 0.000 1.0000
scale(DS) 8.562e-02 4.885e-02 1.880e+02 1.753 0.0813 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(DS) 0.000 m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12653
Scaled residuals:
Min 1Q Median 3Q Max
-2.6156 -0.6504 -0.2404 0.5367 8.3469
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4405 0.6637
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.732e-15 4.924e-02 1.880e+02 0.000 1.000
scale(SCC) 5.967e-03 4.924e-02 1.880e+02 0.121 0.904
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12651.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.6140 -0.6489 -0.2407 0.5372 8.3493
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4378 0.6617
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.152e-15 4.910e-02 1.880e+02 0.000 1.000
scale(MemSE) 5.180e-02 4.910e-02 1.880e+02 1.055 0.293
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12650.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.6141 -0.6520 -0.2417 0.5372 8.3514
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4343 0.6590
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.433e-15 4.891e-02 1.880e+02 0.000 1.00
scale(PrivCSE) 7.850e-02 4.891e-02 1.880e+02 1.605 0.11
Correlation of Fixed Effects:
(Intr)
scl(PrvCSE) 0.000 m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12653
Scaled residuals:
Min 1Q Median 3Q Max
-2.6155 -0.6501 -0.2406 0.5369 8.3474
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4405 0.6637
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -9.156e-15 4.924e-02 1.880e+02 0.000 1.000
scale(PubCSE) 7.534e-03 4.924e-02 1.880e+02 0.153 0.879
Correlation of Fixed Effects:
(Intr)
scal(PbCSE) 0.000 m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 12651.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.6140 -0.6489 -0.2407 0.5372 8.3493
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.4378 0.6617
Residual 0.5641 0.7511
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.152e-15 4.910e-02 1.880e+02 0.000 1.000
scale(MemSE) 5.180e-02 4.910e-02 1.880e+02 1.055 0.293
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 Individual Differences Predict Pairwise Overlap in Identities in Common
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.3269 -0.5946 -0.2387 0.4702 3.8500
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3886 0.6234
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.653e-15 4.649e-02 1.880e+02 0.000 1.000
scale(SCC) 9.345e-03 4.649e-02 1.880e+02 0.201 0.841
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3256 -0.5957 -0.2379 0.4697 3.8522
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3883 0.6231
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.541e-15 4.647e-02 1.880e+02 0.000 1.000
scale(Ind) -2.007e-02 4.647e-02 1.880e+02 -0.432 0.666
Correlation of Fixed Effects:
(Intr)
scale(Ind) 0.000 m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3318 -0.5988 -0.2342 0.4684 3.8557
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3873 0.6224
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.377e-15 4.641e-02 1.880e+02 0.000 1.000
scale(Inter) 3.678e-02 4.642e-02 1.880e+02 0.792 0.429
Correlation of Fixed Effects:
(Intr)
scale(Intr) 0.000 m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.3263 -0.5977 -0.2349 0.4672 3.8540
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3871 0.6222
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.995e-15 4.640e-02 1.880e+02 0.000 1.000
scale(SWLS) 3.974e-02 4.641e-02 1.880e+02 0.856 0.393
Correlation of Fixed Effects:
(Intr)
scale(SWLS) 0.000 m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3256 -0.5961 -0.2386 0.4705 3.8518
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3884 0.6232
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.121e-15 4.647e-02 1.880e+02 0.000 1.000
scale(IdImp) 1.827e-02 4.648e-02 1.880e+02 0.393 0.695
Correlation of Fixed Effects:
(Intr)
scal(IdImp) 0.000 m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(phi) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13072.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.3258 -0.6028 -0.2348 0.4654 3.8616
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3771 0.6141
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.439e-15 4.583e-02 1.880e+02 0.00 1.0000
scale(phi) -1.073e-01 4.583e-02 1.880e+02 -2.34 0.0203 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(phi) 0.000 m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13050.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.3427 -0.6071 -0.2309 0.4698 3.8628
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3339 0.5778
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.790e-15 4.328e-02 1.880e+02 0.000 1
scale(overlap_norm) 2.329e-01 4.328e-02 1.880e+02 5.381 2.19e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(vrlp_n) 0.000 m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13058.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.3461 -0.6160 -0.2261 0.4779 3.8683
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3494 0.5911
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.634e-15 4.421e-02 1.880e+02 0.00 1
scale(H_index) 1.972e-01 4.422e-02 1.880e+02 4.46 1.41e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scal(H_ndx) 0.000 m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.3270 -0.5958 -0.2379 0.4697 3.8522
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3886 0.6233
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.598e-15 4.648e-02 1.880e+02 0.000 1.000
scale(SE) 1.233e-02 4.649e-02 1.880e+02 0.265 0.791
Correlation of Fixed Effects:
(Intr)
scale(SE) 0.000 m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13076.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.3295 -0.5963 -0.2367 0.4688 3.8576
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3860 0.6213
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.977e-15 4.634e-02 1.880e+02 0.000 1.000
scale(NFC) -5.217e-02 4.634e-02 1.880e+02 -1.126 0.262
Correlation of Fixed Effects:
(Intr)
scale(NFC) 0.000 m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(DS) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.3267 -0.5950 -0.2378 0.4711 3.8518
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3885 0.6233
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.254e-15 4.648e-02 1.880e+02 0.000 1.000
scale(DS) 1.531e-02 4.648e-02 1.880e+02 0.329 0.742
Correlation of Fixed Effects:
(Intr)
scale(DS) 0.000 m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.3269 -0.5946 -0.2387 0.4702 3.8500
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3886 0.6234
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.653e-15 4.649e-02 1.880e+02 0.000 1.000
scale(SCC) 9.345e-03 4.649e-02 1.880e+02 0.201 0.841
Correlation of Fixed Effects:
(Intr)
scale(SCC) 0.000 m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.3261 -0.5965 -0.2374 0.4684 3.8533
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3881 0.6229
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.955e-15 4.646e-02 1.880e+02 0.000 1.000
scale(MemSE) -2.533e-02 4.646e-02 1.880e+02 -0.545 0.586
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13076.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.3289 -0.5986 -0.2373 0.4683 3.8576
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3857 0.6211
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.272e-15 4.632e-02 1.880e+02 0.00 1.000
scale(PrivCSE) -5.422e-02 4.633e-02 1.880e+02 -1.17 0.243
Correlation of Fixed Effects:
(Intr)
scl(PrvCSE) 0.000 m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.3265 -0.5952 -0.2387 0.4698 3.8517
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3886 0.6234
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -5.276e-15 4.649e-02 1.880e+02 0.000 1.000
scale(PubCSE) -1.082e-02 4.649e-02 1.880e+02 -0.233 0.816
Correlation of Fixed Effects:
(Intr)
scal(PbCSE) 0.000 m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
Data: idSim2
REML criterion at convergence: 13077.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.3261 -0.5965 -0.2374 0.4684 3.8533
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3881 0.6229
Residual 0.6154 0.7845
Number of obs: 5320, groups: subID, 190
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -6.955e-15 4.646e-02 1.880e+02 0.000 1.000
scale(MemSE) -2.533e-02 4.646e-02 1.880e+02 -0.545 0.586
Correlation of Fixed Effects:
(Intr)
scale(MmSE) 0.000 More trait overlap predicts more intergroup bias
Study 2 Only
m<-lmer(scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + ( scale(traitCommNod) + scale(idCommNod) | subID) + ( 1 | id), data=idShort2)
summary(m)Identity Centrality Predicts Less Mutability
Study 1 Only
for(i in 1:length(networkMeasures)){
corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
assign(paste0(networkMeasures[i],".CorDf"),corsDf)
assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
print(corsDf)
print(barsDf)
}
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Positivity of Identity Associated with Greater Intergroup Bias
Study 2 Only
More positive self-reported
m<-lmer(scale(interG) ~ scale(pos) + ( scale(pos) | subID) + ( 1 | id), data=idShort2)
summary(m)More positive traits associated with identity than negative
m<-lmer(scale(interG) ~ scale(pndiff) + ( scale(pndiff) | subID) + ( 1 | id), data=idShort2)
summary(m)Differentiation and Inclusion Quadratic Association
Study 2 Only
m<-lmer(scale(differ) ~ scale(poly(inclus, 2)) + ( scale(inclus) | subID) + ( 1 | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c( "inclus" )) %>% plot(show.title=FALSE)Differentiation and Inclusion Interact in Predicting Identification
Study 2 Only
m<-lmer(scale(streng) ~ scale(inclus) * scale(differ) + ( scale(inclus) + scale(differ) | subID) + ( 1 | id), data=idShort2)
summary(m)
ggpredict(m, c( "inclus" , "differ")) %>% plot(show.title=FALSE)m<-lmer(scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) + ( scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + ( 1 | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c( "inclus[all]" , "differ[all]")) %>% plot(show.title=FALSE)Normative Latino Ratings
normTraits <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/Normative/normativeDfStudy12.csv", header = T)
fullLong1s <- fullLong1 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s <- fullLong2 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s$subID <- fullLong2$subID + 100000
combLong <- rbind(fullLong1s,fullLong2s)
combLong <- merge(combLong, normTraits, by = "traits")
combLong$ynLatin <- ifelse(combLong$idC==2 & combLong$id=="Race", "HL", "Not HL")
combLong$ynAsian <- ifelse(combLong$idC==4 & combLong$id=="Race", "As", "Not As")
combLong$ynMale <- ifelse(combLong$idC==1 & combLong$id=="Gen", "M", "Not M")
combLong$ynFemale <- ifelse(combLong$idC==2 & combLong$id=="Gen", "F", "Not F")
combLong$ynHetero <- ifelse(combLong$idC==1 & combLong$id=="Sex", "Het", "Not Het")
combLong$ynBis <- ifelse(combLong$idC==3 & combLong$id=="Sex", "Bi", "Not Bi")
combLong$ynCath <- ifelse(combLong$idC==1 & combLong$id=="Rel", "Cath", "Not Cath")
combLong$ynChrist <- ifelse(combLong$idC==2 & combLong$id=="Rel", "Christ", "Not Christ")
combLong$ynAgnos <- ifelse(combLong$idC==8 & combLong$id=="Rel", "Agn", "Not Agn")
combLong$ynAthei <- ifelse(combLong$idC==9 & combLong$id=="Rel", "Ath", "Not Ath")
combLong$ynDem <- ifelse(combLong$idC==1 & combLong$id=="Pol", "Dem", "Not Dem")
m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale*streng + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
Error in eval(predvars, data, env) : object 'streng' not 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